Few-shot segmentation (FSS) aims to segment objects of new categories given only a handful of annotated samples. Previous works focus their efforts on exploring the support information while paying less attention to the mining of the critical query branch. In this paper, we rethink the importance of support information and propose a new query-centric FSS model Adversarial Mining Transformer (AMFormer), which achieves accurate query image segmentation with only rough support guidance or even weak support labels. The proposed AMFormer enjoys several merits. First, we design an object mining transformer (G) that can achieve the expansion of incomplete region activated by support clue, and a detail mining transformer (D) to discriminate the detailed local difference between the expanded mask and the ground truth. Second, we propose to train G and D via an adversarial process, where G is optimized to generate more accurate masks approaching ground truth to fool D. We conduct extensive experiments on commonly used Pascal-5i and COCO-20i benchmarks and achieve state-of-the-art results across all settings. In addition, the decent performance with weak support labels in our query-centric paradigm may inspire the development of more general FSS models. Code will be available at https://github.com/Wyxdm/AMNet.

翻译：少样本分割旨在新类别中仅凭少量标注样本分割目标。先前工作集中于探索支撑信息，而对关键查询分支的挖掘关注不足。本文重新思考了支撑信息的重要性，并提出了一种以查询为中心的少样本分割模型——对抗挖掘Transformer，该模型仅需粗略的支撑指导甚至弱支撑标签即可实现精确的查询图像分割。所提出的AMFormer具有以下优势：第一，我们设计了一个目标挖掘Transformer，可扩展由支撑线索激活的不完整区域，以及一个细节挖掘Transformer，用于判别扩展掩码与真实标注之间的局部细节差异；第二，我们提出通过对抗过程训练G和D，其中G被优化以生成更接近真实标注的精确掩码来欺骗D。我们在广泛使用的Pascal-5i和COCO-20i基准上进行了大量实验，在所有设置下均取得了最先进的结果。此外，在以查询为中心的范式下，使用弱支撑标签时取得的可观性能可能启发更通用的少样本分割模型的发展。代码将发布于https://github.com/Wyxdm/AMNet。